function [rmse_MC, ixx] = filt_mc_(OutDir)
% function [rmse_MC, ixx] = filt_mc_(OutDir)
% copyright Marco Ratto 2006
% inputs (from opt_gsa structure)
% vvarvecm = options_gsa_.var_rmse;
% loadSA   = options_gsa_.load_rmse;
% pfilt    = options_gsa_.pfilt_rmse;
% alpha    = options_gsa_.alpha_rmse; 
% alpha2   = options_gsa_.alpha2_rmse; 
% istart   = options_gsa_.istart_rmse;
% alphaPC  = 0.5;
%
% Part of the Sensitivity Analysis Toolbox for DYNARE
%
% Written by Marco Ratto, 2006
% Joint Research Centre, The European Commission,
% (http://eemc.jrc.ec.europa.eu/),
% marco.ratto@jrc.it 
%
% Disclaimer: This software is not subject to copyright protection and is in the public domain. 
% It is an experimental system. The Joint Research Centre of European Commission 
% assumes no responsibility whatsoever for its use by other parties
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
% characteristic. We would appreciate acknowledgement if the software is used.
% Reference:
% M. Ratto, Global Sensitivity Analysis for Macroeconomic models, MIMEO, 2006.
%

global bayestopt_ estim_params_ M_ options_ oo_

options_gsa_=options_.opt_gsa;
vvarvecm = options_gsa_.var_rmse;
loadSA   = options_gsa_.load_rmse;
pfilt    = options_gsa_.pfilt_rmse;
alpha    = options_gsa_.alpha_rmse; 
alpha2   = options_gsa_.alpha2_rmse; 
istart   = options_gsa_.istart_rmse;
alphaPC  = 0.5;
  
fname_ = M_.fname;
lgy_ = M_.endo_names;
dr_ = oo_.dr;

disp(' ')
disp(' ')
disp('Starting sensitivity analysis')
disp('for the fit of EACH observed series ...')
disp(' ')
disp('Deleting old SA figures...')
a=dir([OutDir,'\*.*']);
tmp1='0';
if options_.opt_gsa.ppost,
  tmp=['_rmse_post'];
else
  if options_.opt_gsa.pprior
    tmp=['_rmse_prior'];
  else
    tmp=['_rmse_mc'];
  end
  if options_gsa_.lik_only,
    tmp1 = [tmp,'_post_SA'];
    tmp = [tmp,'_lik_SA'];
  end
end
for j=1:length(a), 
  if strmatch([fname_,tmp],a(j).name), 
    disp(a(j).name)
    delete([OutDir,'\',a(j).name])
  end, 
  if strmatch([fname_,tmp1],a(j).name), 
    disp(a(j).name)
    delete([OutDir,'\',a(j).name])
  end, 
end
disp('done !')


nshock=estim_params_.nvx + estim_params_.nvn + estim_params_.ncx + estim_params_.ncn;
npar=estim_params_.np;
load(options_.mode_file,'xparam1')
if options_.opt_gsa.ppost,
  c=load([fname_,'_mean'],'xparam1');
  xparam1_mean=c.xparam1;
  clear c
elseif ~isempty(ls([fname_,'_mean.mat']))
  c=load([fname_,'_mean'],'xparam1');
  xparam1_mean=c.xparam1;
  clear c
end

if options_.opt_gsa.ppost,
  fnamtmp=[fname_,'_post'];
  DirectoryName = CheckPath('metropolis');
else
  if options_.opt_gsa.pprior
    fnamtmp=[fname_,'_prior'];
  else
    fnamtmp=[fname_,'_mc'];      
  end
end
if ~loadSA,
  if exist('xparam1','var')
    set_all_parameters(xparam1);
    steady_;
    ys_mode=oo_.steady_state;
  end
  if exist('xparam1_mean','var')
    set_all_parameters(xparam1_mean);
    steady_;
    ys_mean=oo_.steady_state;
  end
%   eval(options_.datafile)
  obs = dat_fil_(options_.datafile);
  if ~options_.opt_gsa.ppost
    load([OutDir,'\',fnamtmp],'x','logpo2','stock_gend','stock_data');
    logpo2=-logpo2;
  else
    %load([DirectoryName '/' M_.fname '_data.mat']);
    [stock_gend, stock_data] = read_data;
    filfilt = dir([DirectoryName '/' M_.fname '_filter*.mat']);
    filparam = dir([DirectoryName '/' M_.fname '_param*.mat']);
    x=[];
    logpo2=[];
    sto_ys=[];
    for j=1:length(filparam),
      %load([DirectoryName '/' M_.fname '_param',int2str(j),'.mat']);
      if isempty(strmatch([M_.fname '_param_irf'],filparam(j).name))
        load([DirectoryName '/' filparam(j).name]);
        x=[x; stock]; 
        logpo2=[logpo2; stock_logpo];
        sto_ys=[sto_ys; stock_ys];
        clear stock stock_logpo stock_ys;
      end
    end
  end
  nruns=size(x,1);
  nfilt=floor(pfilt*nruns);
  if options_.opt_gsa.ppost | (options_.opt_gsa.ppost==0 & options_.opt_gsa.lik_only==0)
  disp(' ')
  disp('Computing RMSE''s...')
  fobs = options_.first_obs;
  nobs=options_.nobs;
  for i=1:size(vvarvecm,1),
    vj=deblank(vvarvecm(i,:));
    eval(['vobs =obs.',vj,'(fobs:fobs-1+nobs);'])
    if options_.prefilter == 1
      %eval([vj,'=',vj,'-bayestopt_.mean_varobs(i);'])
      %eval([vj,'=',vj,'-mean(',vj,',1);'])
      vobs = vobs-mean(vobs,1);
    end
    
    jxj = strmatch(vj,lgy_(dr_.order_var,:),'exact');
    js = strmatch(vj,lgy_,'exact');
    if exist('xparam1','var')
      eval(['rmse_mode(i) = sqrt(mean((vobs(istart:end)-oo_.steady_state(js)-oo_.FilteredVariables.',vj,'(istart:end-1)).^2));'])
    end
    y0=zeros(nobs+1,nruns);
    if options_.opt_gsa.ppost
      %y0=zeros(nobs+max(options_.filter_step_ahead),nruns);
      nbb=0;
      for j=1:length(filfilt),
        load([DirectoryName '/' M_.fname '_filter',num2str(j),'.mat']);
        nb = size(stock,4);
%         y0(:,nbb+1:nbb+nb)=squeeze(stock(1,jxj,:,:)) + ...
%           kron(sto_ys(nbb+1:nbb+nb,js)',ones(size(stock,3),1));
        y0(:,nbb+1:nbb+nb)=squeeze(stock(1,jxj,1:nobs+1,:)) + ...
          kron(sto_ys(nbb+1:nbb+nb,js)',ones(nobs+1,1));
        %y0(:,:,size(y0,3):size(y0,3)+size(stock,3))=stock;
        nbb=nbb+nb;
        clear stock;
      end
    else
      filfilt=ls([OutDir,'\',fnamtmp,'_*.mat']);
      nbb=0;
      for j=1:size(filfilt,1),
        load([OutDir,'\',fnamtmp,'_',num2str(j),'.mat'],'stock_filter','stock_ys');
        nb = size(stock_filter,3);
        y0(:,nbb+1:nbb+nb) = squeeze(stock_filter(jxj,:,:)) + ...
          kron(stock_ys(:,js)',ones(nobs+1,1));
        %y0(:,:,size(y0,3):size(y0,3)+size(stock,3))=stock;
        nbb=nbb+nb;
        clear stock_filter;
      end

%       y0 = squeeze(stock_filter(:,jxj,:)) + ...
%         kron(stock_ys(js,:),ones(size(stock_filter,1),1));
    end
    y0M=mean(y0,2);
    for j=1:nruns,
      rmse_MC(j,i) = sqrt(mean((vobs(istart:end)-y0(istart:end-1,j)).^2));
    end
    if exist('xparam1_mean','var')
      %eval(['rmse_pmean(i) = sqrt(mean((',vj,'(fobs-1+istart:fobs-1+nobs)-y0M(istart:end-1)).^2));'])
      [alphahat,etahat,epsilonhat,ahat,SteadyState,trend_coeff,aK] = DsgeSmoother(xparam1_mean,stock_gend,stock_data);
      y0 = ahat(jxj,:)' + ...
        kron(ys_mean(js,:),ones(size(ahat,2),1));
      rmse_pmean(i) = sqrt(mean((vobs(istart:end)-y0(istart:end-1)).^2));
    end
  end
  clear stock_filter;
  end
  for j=1:nruns,
    lnprior(j,1) = priordens(x(j,:),bayestopt_.pshape,bayestopt_.p1,bayestopt_.p2,bayestopt_.p3,bayestopt_.p4);
  end
  likelihood=logpo2(:)-lnprior(:);
  disp('... done!')
  
  if options_.opt_gsa.ppost
    save([OutDir,'\',fnamtmp], 'x', 'logpo2', 'likelihood', 'rmse_MC', 'rmse_mode','rmse_pmean')    
  else
    if options_.opt_gsa.lik_only
      save([OutDir,'\',fnamtmp], 'likelihood', '-append')    
    else
      if exist('xparam1_mean','var')
        save([OutDir,'\',fnamtmp], 'likelihood', 'rmse_MC', 'rmse_mode','rmse_pmean','-append')    
      else
        save([OutDir,'\',fnamtmp], 'likelihood', 'rmse_MC', 'rmse_mode','-append')    
      end
    end
  end
else
  if options_.opt_gsa.lik_only & options_.opt_gsa.ppost==0
    load([OutDir,'\',fnamtmp],'x','logpo2','likelihood');
  else
    load([OutDir,'\',fnamtmp],'x','logpo2','likelihood','rmse_MC','rmse_mode','rmse_pmean');
  end
  lnprior=logpo2(:)-likelihood(:);
  nruns=size(x,1);
  nfilt=floor(pfilt*nruns);
end
% smirnov tests
nfilt0=nfilt*ones(size(vvarvecm,1),1);
logpo2=logpo2(:);
if ~options_.opt_gsa.ppost
  [dum, ipost]=sort(-logpo2);
  [dum, ilik]=sort(-likelihood);
end
if ~options_.opt_gsa.ppost & options_.opt_gsa.lik_only
  if options_.opt_gsa.pprior
    anam='rmse_prior_post';
  else
    anam='rmse_mc_post';
  end
  stab_map_1(x, ipost(1:nfilt), ipost(nfilt+1:end), anam, 1,[],OutDir);
  stab_map_2(x(ipost(1:nfilt),:),alpha2,anam, OutDir);
  if options_.opt_gsa.pprior
    anam='rmse_prior_lik';
  else
    anam='rmse_mc_lik';
  end
  stab_map_1(x, ilik(1:nfilt), ilik(nfilt+1:end), anam, 1,[],OutDir);
  stab_map_2(x(ilik(1:nfilt),:),alpha2,anam, OutDir);
else
  for i=1:size(vvarvecm,1),
  [dum, ixx(:,i)]=sort(rmse_MC(:,i));
  if options_.opt_gsa.ppost,
    %nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
    rmse_txt=rmse_pmean;
  else
    if options_.opt_gsa.pprior | ~exist('rmse_pmean'),
      rmse_txt=rmse_mode;
    else
      %nfilt0(i)=length(find(rmse_MC(:,i)<rmse_pmean(i)));
      rmse_txt=rmse_pmean;
    end
  end
  for j=1:npar+nshock,
    [H,P,KSSTAT] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j), alpha);
    [H1,P1,KSSTAT1] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,1);
    [H2,P2,KSSTAT2] = smirnov(x(ixx(nfilt0(i)+1:end,i),j),x(ixx(1:nfilt0(i),i),j),alpha,-1);
    if H1 & H2==0,
      SS(j,i)=1;      
    elseif H1==0,
      SS(j,i)=-1;     
    else
      SS(j,i)=0;     
    end
    PP(j,i)=P;
  end
end
ifig=0;
for i=1:size(vvarvecm,1),
  if mod(i,9)==1,
    ifig=ifig+1;
    figure('name',['Prior ',int2str(ifig)])
  end
  subplot(3,3,i-9*(ifig-1))
  h=cumplot(lnprior(ixx(1:nfilt0(i),i)));
  set(h,'color','red')
  hold on, cumplot(lnprior)
  h=cumplot(lnprior(ixx(nfilt0(i)+1:end,i)));
  set(h,'color','green')
  title(vvarvecm(i,:))
  if mod(i,9)==0 | i==size(vvarvecm,1)
    if options_.opt_gsa.ppost
      saveas(gcf,[OutDir,'\',fname_,'_rmse_post_lnprior',int2str(ifig)])
      eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_post_lnprior',int2str(ifig)]);
      eval(['print -dpdf ' OutDir '\' fname_ '_rmse_post_lnprior',int2str(ifig)]);
    else
      if options_.opt_gsa.pprior
        saveas(gcf,[OutDir,'\',fname_,'_rmse_prior_lnprior',int2str(ifig)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_prior_lnprior',int2str(ifig)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_prior_lnprior',int2str(ifig)]);
      else
        saveas(gcf,[OutDir,'\',fname_,'_rmse_mc_lnprior',int2str(ifig)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_mc_lnprior',int2str(ifig)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_mc_lnprior',int2str(ifig)]);
      end
    end
    close(gcf)
  end
end
ifig=0;
for i=1:size(vvarvecm,1),
  if mod(i,9)==1,
    ifig=ifig+1;
    figure('name',['Likelihood ',int2str(ifig)])
  end
  subplot(3,3,i-9*(ifig-1))
  h=cumplot(likelihood(ixx(1:nfilt0(i),i)));
  set(h,'color','red')
  hold on, h=cumplot(likelihood);
  h=cumplot(likelihood(ixx(nfilt0(i)+1:end,i)));
  set(h,'color','green')
  title(vvarvecm(i,:))
  if options_.opt_gsa.ppost==0,
    set(gca,'xlim',[min( likelihood(ixx(1:nfilt0(i),i)) ) max( likelihood(ixx(1:nfilt0(i),i)) )])
  end
  if mod(i,9)==0 | i==size(vvarvecm,1)
    if options_.opt_gsa.ppost
      saveas(gcf,[OutDir,'\',fname_,'_rmse_post_lnlik',int2str(ifig)])
      eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_post_lnlik',int2str(ifig)]);
      eval(['print -dpdf ' OutDir '\' fname_ '_rmse_post_lnlik',int2str(ifig)]);
    else
      if options_.opt_gsa.pprior
        saveas(gcf,[OutDir,'\',fname_,'_rmse_prior_lnlik',int2str(ifig)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_prior_lnlik',int2str(ifig)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_prior_lnlik',int2str(ifig)]);
      else
        saveas(gcf,[OutDir,'\',fname_,'_rmse_mc_lnlik',int2str(ifig)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_mc_lnlik',int2str(ifig)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_mc_lnlik',int2str(ifig)]);
      end
    end
    close(gcf)
  end
end
ifig=0;
for i=1:size(vvarvecm,1),
  if mod(i,9)==1,
    ifig=ifig+1;
    figure('name',['Posterior ',int2str(ifig)])
  end
  subplot(3,3,i-9*(ifig-1))
  h=cumplot(logpo2(ixx(1:nfilt0(i),i)));
  set(h,'color','red')
  hold on, h=cumplot(logpo2);
  h=cumplot(logpo2(ixx(nfilt0(i)+1:end,i)));
  set(h,'color','green')
  title(vvarvecm(i,:))
  if options_.opt_gsa.ppost==0,
    set(gca,'xlim',[min( logpo2(ixx(1:nfilt0(i),i)) ) max( logpo2(ixx(1:nfilt0(i),i)) )])
  end
  if mod(i,9)==0 | i==size(vvarvecm,1)
    if options_.opt_gsa.ppost
      saveas(gcf,[OutDir,'\',fname_,'_rmse_post_lnpost',int2str(ifig)])
      eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_post_lnpost',int2str(ifig)]);
      eval(['print -dpdf ' OutDir '\' fname_ '_rmse_post_lnpost',int2str(ifig)]);
    else
      if options_.opt_gsa.pprior
        saveas(gcf,[OutDir,'\',fname_,'_rmse_prior_lnpost',int2str(ifig)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_prior_lnpost',int2str(ifig)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_prior_lnpost',int2str(ifig)]);
      else
        saveas(gcf,[OutDir,'\',fname_,'_rmse_mc_lnpost',int2str(ifig)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_mc_lnpost',int2str(ifig)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_mc_lnpost',int2str(ifig)]);
      end
    end
    close(gcf)
  end
end

param_names='';
for j=1:npar+nshock,
  param_names=str2mat(param_names, bayestopt_.name{j});
end
param_names=param_names(2:end,:);

disp(' ')
disp('RMSE over the MC sample:')
disp('            min yr RMSE    max yr RMSE')
for j=1:size(vvarvecm,1),
  disp([vvarvecm(j,:), sprintf('%15.5g',[(min(rmse_MC(:,j))) [(max(rmse_MC(:,j)))]])])
end
invar = find( std(rmse_MC)./mean(rmse_MC)<=0.0001 );
if ~isempty(invar)
  disp(' ')
  disp(' ')
  disp('RMSE is not varying significantly over the MC sample for the following variables:')
  disp(vvarvecm(invar,:))
  disp('These variables are excluded from SA')
  disp('[Unless you treat these series as exogenous, there is something wrong in your estimation !]')
end
ivar = find( std(rmse_MC)./mean(rmse_MC)>0.0001 );
vvarvecm=vvarvecm(ivar,:);
rmse_MC=rmse_MC(:,ivar);

disp(' ')
% if options_.opt_gsa.ppost==0 & options_.opt_gsa.pprior,
  disp(['Sample filtered the ',num2str(pfilt*100),'% best RMSE''s for each observed series ...' ])
% else
%   disp(['Sample filtered the best RMSE''s smaller than RMSE at the posterior mean ...' ])
% end
% figure, boxplot(rmse_MC)
% set(gca,'xticklabel',vvarvecm)
% saveas(gcf,[fname_,'_SA_RMSE'])

disp(' ')
disp(' ')
disp('RMSE ranges after filtering:')
if options_.opt_gsa.ppost==0 & options_.opt_gsa.pprior,
  disp(['             best ',num2str(pfilt*100),'% filtered             remaining 90%'])
  disp(['             min            max            min            max            posterior mode'])
else
  disp(['             best  filtered             remaining '])
  disp(['             min            max            min            max            posterior mean'])
end
for j=1:size(vvarvecm,1),
  disp([vvarvecm(j,:), sprintf('%15.5g',[min(rmse_MC(ixx(1:nfilt0(j),j),j)) ...
        max(rmse_MC(ixx(1:nfilt0(j),j),j))  ...
        min(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
        max(rmse_MC(ixx(nfilt0(j)+1:end,j),j)) ...
        rmse_txt(j)])])
  %   disp([vvarvecm(j,:), sprintf('%15.5g',[min(logpo2(ixx(1:nfilt,j))) ...
  %         max(logpo2(ixx(1:nfilt,j)))  ...
  %         min(logpo2(ixx(nfilt+1:end,j))) ...
  %         max(logpo2(ixx(nfilt+1:end,j)))])])
end

SP=zeros(npar+nshock,size(vvarvecm,1));
for j=1:size(vvarvecm,1),
  ns=find(PP(:,j)<alpha);
  SP(ns,j)=ones(size(ns));
  SS(:,j)=SS(:,j).*SP(:,j);
end

for j=1:npar+nshock, %estim_params_.np,
  nsp(j)=length(find(SP(j,:)));
end
snam0=param_names(find(nsp==0),:);
snam1=param_names(find(nsp==1),:);
snam2=param_names(find(nsp>1),:);
snam=param_names(find(nsp>0),:);
% snam0=bayestopt_.name(find(nsp==0));
% snam1=bayestopt_.name(find(nsp==1));
% snam2=bayestopt_.name(find(nsp>1));
% snam=bayestopt_.name(find(nsp>0));
nsnam=(find(nsp>1));

disp(' ')
disp(' ')
disp('These parameters do not affect significantly the fit of ANY observed series:')
disp(snam0)
disp(' ')
disp('These parameters affect ONE single observed series:')
disp(snam1)
disp(' ')
disp('These parameters affect MORE THAN ONE observed series: trade off exists!')
disp(snam2)


%pnam=bayestopt_.name(end-estim_params_.np+1:end);
pnam=bayestopt_.name;

% plot trade-offs
a00=jet(size(vvarvecm,1));
for ix=1:ceil(length(nsnam)/5),
  figure,
  for j=1+5*(ix-1):min(size(snam2,1),5*ix),
    subplot(2,3,j-5*(ix-1))
    %h0=cumplot(x(:,nsnam(j)+nshock));
    h0=cumplot(x(:,nsnam(j)));
    set(h0,'color',[0 0 0])
    hold on,
    np=find(SP(nsnam(j),:));
    %a0=jet(nsp(nsnam(j)));
    a0=a00(np,:);
    for i=1:nsp(nsnam(j)), %size(vvarvecm,1),
      %h0=cumplot(x(ixx(1:nfilt,np(i)),nsnam(j)+nshock));
      h0=cumplot(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)));
      set(h0,'color',a0(i,:))
    end
    ydum=get(gca,'ylim');
    %xdum=xparam1(nshock+nsnam(j));
    xdum=xparam1(nsnam(j));
    h1=plot([xdum xdum],ydum);
    set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
    xlabel('')
    title([pnam{nsnam(j)}],'interpreter','none')
  end
  %subplot(3,2,6)
    h0=legend(str2mat('base',vvarvecm(np,:)),0); 
    set(h0,'fontsize',6,'position',[0.7 0.1 0.2 0.3],'interpreter','none')
    %h0=legend({'base',vnam{np}}',0); 
    %set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
    if options_.opt_gsa.ppost
      saveas(gcf,[OutDir,'\',fname_,'_rmse_post_',num2str(ix)])
      eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_post_' int2str(ix)]);
      eval(['print -dpdf ' OutDir '\' fname_ '_rmse_post_' int2str(ix)]);
    else
      if options_.opt_gsa.pprior
        saveas(gcf,[OutDir,'\',fname_,'_rmse_prior_',num2str(ix)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_prior_' int2str(ix)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_prior_' int2str(ix)]);
      else
        saveas(gcf,[OutDir,'\',fname_,'_rmse_mc_',num2str(ix)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_mc_' int2str(ix)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_mc_' int2str(ix)]);
      end
    end
end
close all

for j=1:size(SP,2),
  nsx(j)=length(find(SP(:,j)));
end

number_of_grid_points = 2^9;      % 2^9 = 512 !... Must be a power of two.
bandwidth = 0;                    % Rule of thumb optimal bandwidth parameter.
kernel_function = 'gaussian';     % Gaussian kernel for Fast Fourrier Transform approximaton.  
%kernel_function = 'uniform';     % Gaussian kernel for Fast Fourrier Transform approximaton.  

for ix=1:ceil(length(nsnam)/5),
  figure,
  for j=1+5*(ix-1):min(size(snam2,1),5*ix),
    subplot(2,3,j-5*(ix-1))
    optimal_bandwidth = mh_optimal_bandwidth(x(:,nsnam(j)),size(x,1),bandwidth,kernel_function); 
    [x1,f1] = kernel_density_estimate(x(:,nsnam(j)),number_of_grid_points,...
        optimal_bandwidth,kernel_function);
    h0 = plot(x1, f1,'k');
    hold on,
    np=find(SP(nsnam(j),:));
    %a0=jet(nsp(nsnam(j)));
    a0=a00(np,:);
    for i=1:nsp(nsnam(j)), %size(vvarvecm,1),
      optimal_bandwidth = mh_optimal_bandwidth(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)),nfilt,bandwidth,kernel_function); 
      [x1,f1] = kernel_density_estimate(x(ixx(1:nfilt0(np(i)),np(i)),nsnam(j)),number_of_grid_points,...
          optimal_bandwidth,kernel_function);
      h0 = plot(x1, f1);
      set(h0,'color',a0(i,:))
    end
    ydum=get(gca,'ylim');
    set(gca,'ylim',[0 ydum(2)]);
    %xdum=xparam1(nshock+nsnam(j));
    xdum=xparam1(nsnam(j));
    h1=plot([xdum xdum],[0 ydum(2)]);
    set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
    xlabel('')
    title([pnam{nsnam(j)}],'interpreter','none')
  end
    h0=legend(str2mat('base',vvarvecm(np,:)),0); 
    set(h0,'fontsize',6,'position',[0.7 0.1 0.2 0.3],'interpreter','none')
    %h0=legend({'base',vnam{np}}',0); 
    %set(findobj(get(h0,'children'),'type','text'),'interpreter','none')
    if options_.opt_gsa.ppost
      saveas(gcf,[OutDir,'\',fname_,'_rmse_post_dens_',num2str(ix)])
      eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_post_dens_' int2str(ix)]);
      eval(['print -dpdf ' OutDir '\' fname_ '_rmse_post_dens_' int2str(ix)]);
    else
      if options_.opt_gsa.pprior
        saveas(gcf,[OutDir,'\',fname_,'_rmse_prior_dens_',num2str(ix)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_prior_dens_' int2str(ix)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_prior_dens_' int2str(ix)]);
      else
        saveas(gcf,[OutDir,'\',fname_,'_rmse_mc_dens_',num2str(ix)])
        eval(['print -depsc2 ' OutDir '\' fname_ '_rmse_mc_dens_' int2str(ix)]);
        eval(['print -dpdf ' OutDir '\' fname_ '_rmse_mc_dens_' int2str(ix)]);
      end
    end
end
close all

% for j=1:size(SP,2),
%     nfig=0;
%     np=find(SP(:,j));
%     for i=1:nsx(j), %size(vvarvecm,1),
%         if mod(i,12)==1,
%             nfig=nfig+1;
%             %figure('name',['Sensitivity of fit of ',vnam{j}]),
%             figure('name',['Sensitivity of fit of ',deblank(vvarvecm(j,:)),' ',num2str(nfig)]),
%         end
%         
%         subplot(3,4,i-12*(nfig-1))
%         optimal_bandwidth = mh_optimal_bandwidth(x(ixx(1:nfilt,j),np(i)),nfilt,bandwidth,kernel_function); 
%         [x1,f1] = kernel_density_estimate(x(ixx(1:nfilt,j),np(i)),number_of_grid_points,...
%             optimal_bandwidth,kernel_function);
%         plot(x1, f1,':k','linewidth',2)
%         optimal_bandwidth = mh_optimal_bandwidth(x(ixx(nfilt+1:end,j),np(i)),nruns-nfilt,bandwidth,kernel_function); 
%         [x1,f1] = kernel_density_estimate(x(ixx(nfilt+1:end,j),np(i)),number_of_grid_points,...
%             optimal_bandwidth,kernel_function);
%         hold on, plot(x1, f1,'k','linewidth',2)
%         ydum=get(gca,'ylim');
%         %xdum=xparam1(nshock+np(i));
%         xdum=xparam1(np(i));
%         h1=plot([xdum xdum],ydum);
%         set(h1,'color',[0.85 0.85 0.85],'linewidth',2)
%         %xdum1=mean(x(ixx(1:nfilt,j),np(i)+nshock));
%         xdum1=mean(x(ixx(1:nfilt,j),np(i)));
%         h2=plot([xdum1 xdum1],ydum);
%         set(h2,'color',[0 1 0],'linewidth',2)
%         %         h0=cumplot(x(nfilt+1:end,np(i)+nshock));
%         %         set(h0,'color',[1 1 1])
%         %         hold on,
%         %         h0=cumplot(x(ixx(1:nfilt,j),np(i)+nshock));
%         %         set(h0,'linestyle',':','color',[1 1 1])
%         %title([pnam{np(i)}])
%         title([pnam{np(i)},'. K-S prob ', num2str(PP(np(i),j))],'interpreter','none')
%         xlabel('')
%         if mod(i,12)==0 | i==nsx(j),
%             saveas(gcf,[fname_,'_rmse_',deblank(vvarvecm(j,:)),'_',int2str(nfig)])
%             close(gcf)
%         end
%     end
% end


disp(' ')
disp(' ')
disp('Sensitivity table (significance and direction):')
vav=char(zeros(1, size(param_names,2)+3 ));
ibl = 12-size(vvarvecm,2);
for j=1:size(vvarvecm,1), 
  vav = [vav, char(zeros(1,ibl)),vvarvecm(j,:)];
end
disp(vav)
for j=1:npar+nshock, %estim_params_.np,
  %disp([param_names(j,:), sprintf('%8.5g',SP(j,:))])    
  disp([param_names(j,:),'   ', sprintf('%12.3g',PP(j,:))])    
  disp([char(zeros(1, size(param_names,2)+3 )),sprintf('    (%6g)',SS(j,:))])    
end


disp(' ')
disp(' ')
disp('Starting bivariate analysis:')

for i=1:size(vvarvecm,1)
  if options_.opt_gsa.ppost
    fnam = ['rmse_post_',deblank(vvarvecm(i,:))];
  else
    if options_.opt_gsa.pprior
      fnam = ['rmse_prior_',deblank(vvarvecm(i,:))];
    else
      fnam = ['rmse_mc_',deblank(vvarvecm(i,:))];
    end
  end
  stab_map_2(x(ixx(1:nfilt0(i),i),:),alpha2,fnam, OutDir);
  
  %     [pc,latent,explained] = pcacov(c0);
  %     %figure, bar([explained cumsum(explained)])
  %     ifig=0;
  %     j2=0;
  %     for j=1:npar+nshock,
  %         i2=find(abs(pc(:,j))>alphaPC);
  %         if ~isempty(i2),
  %             j2=j2+1;
  %             if mod(j2,12)==1,
  %                 ifig=ifig+1;
  %                 figure('name',['PCA of the filtered sample ',deblank(vvarvecm(i,:)),' ',num2str(ifig)]),
  %             end
  %             subplot(3,4,j2-(ifig-1)*12)
  %             bar(pc(i2,j)), 
  %             set(gca,'xticklabel',bayestopt_.name(i2)), 
  %             set(gca,'xtick',[1:length(i2)])
  %             title(['PC ',num2str(j),'. Explained ',num2str(explained(j)),'%'])
  %         end
  %         if (mod(j2,12)==0 | j==(npar+nshock)) & j2,
  %             saveas(gcf,[fname_,'_SA_PCA_',deblank(vvarvecm(i,:)),'_',int2str(ifig)])
  %         end
  %     end
  %     close all
end

end